Density, distribution function, quantile function and random generation for the binomial distribution with parameters
dbinom(x, size, prob, log = FALSE) pbinom(q, size, prob, lower.tail = TRUE, log.p = FALSE) qbinom(p, size, prob, lower.tail = TRUE, log.p = FALSE) rbinom(n, size, prob)
- x, q
- vector of quantiles.
- vector of probabilities.
- number of observations. If
length(n) > 1, the length is taken to be the number required.
- number of trials (zero or more).
- probability of success on each trial.
- log, log.p
- logical; if TRUE, probabilities p are given as log(p).
- logical; if TRUE (default), probabilities are P[X ≤ x], otherwise, P[X > x].
The binomial distribution with
size = n and
prob = p has density for x = 0, ..., n. Note that binomial coefficients can be computed by
choose in R.
If an element of
x is not integer, the result of
dbinom is zero, with a warning. p(x) is computed using Loader's algorithm, see the reference below.
The quantile is defined as the smallest value x such that F(x) ≥ p, where F is the distribution function.
size is not an integer,
NaN is returned. The length of the result is determined by
rbinom, and is the maximum of the lengths of the numerical parameters for the other functions. The numerical parameters other than
n are recycled to the length of the result. Only the first elements of the logical parameters are used.
require(graphics) # Compute P(45 < X < 55) for X Binomial(100,0.5) sum(dbinom(46:54, 100, 0.5)) ## Using "log = TRUE" for an extended range : n <- 2000 k <- seq(0, n, by = 20) plot (k, dbinom(k, n, pi/10, log = TRUE), type = "l", ylab = "log density", main = "dbinom(*, log=TRUE) is better than log(dbinom(*))") lines(k, log(dbinom(k, n, pi/10)), col = "red", lwd = 2) ## extreme points are omitted since dbinom gives 0. mtext("dbinom(k, log=TRUE)", adj = 0) mtext("extended range", adj = 0, line = -1, font = 4) mtext("log(dbinom(k))", col = "red", adj = 1)
Documentation reproduced from R 3.0.2. License: GPL-2.